The overall goal of the OAIC program is to increase scientific knowledge that will lead to effective ways to maintain or restore independence in older Americans. Innovative and appropriate analysis and data management methods are an important and necessary component to the scientific endeavor. A variety of designs, variables, hypotheses, and analyses will be explored, all with the common theme of 'Pathways to Disability'. An analysis core (AC) is proposed with 3 goals - (1) to provide an appropriate data management and analytic resources to the faculty, pilots, and projects in the Pepper Center, (2) to provide a resource in the measurement of physical performance, function, cognition, design and analysis so that appropriate measures will be employed using appropriately designed and analyzed experiments with appropriate conclusions, and (3) to develop innovative biostatistical analytic methodologies. To accomplish these goals, personnel with expertise and experience in data management, analysis, and measurement will direct activities in support ofthe overall Pepper. Both standard and innovative data management methods are proposed and employed, including standardization of analytic methods across studies and construction of data bases with common measures and variables. In addition to provision of technical analytic and data management support, the core will provide consultation, training and research support to the faculty ofthe Pepper Center. The core will also pursue methodologic goals of interest to statisticians and basic scientists. In particular, in order to develop valid and reproducible models ofthe relationship between biomarkers and function, several analytic considerations must be considered, including Type-1 error control for multiple testing, data aggregation, and measurement of change over time in several domains simultaneously. Working closely with the Biological Studies Core (RC2), we will focus on methods for examining trajectories of change in the biological and clinical expression of function, establish temporal ordering in these estimates, assessment of constancy of these relationships across studies, developing appropriate error structures for analysis given complex sampling schemes.